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Agritourism is an expanding form of experience-based rural tourism, yet limited empirical research explains how experiential marketing shapes perceived value and satisfaction in authentic farming contexts. Drawing on Schmitt's Strategic Experiential Modules and the Memorable Tourism Experience (MTE) framework, this study develops and tests a structural model linking agritourism experience, perceived value, and satisfaction. Survey data from 398 visitors across twelve certified agritourist communities in Taiwan were analyzed using CFA and SEM. Results show that agritourism experiences significantly enhance perceived value and directly increase satisfaction, with perceived value exerting a strong mediating effect. From a sustainability perspective, the findings underscore the distinctiveness of agritourism, where authenticity, natural variability, and human-land interactions generate experiential outcomes not replicable in constructed tourism spaces. The study advances experiential marketing theory and offers practical guidance for rural tourism development, thereby supporting sustainable rural development by fostering long-term tourist engagement and local economic vitality.
As artificial intelligence and machine learning advance, increasing privacy concerns and regulatory constraints have limited cross-border data sharing for traditional model training. Federated Learning (FL) offers a privacy-preserving approach by enabling distributed training without exposing raw data. However, FL faces significant challenges, particularly when dealing with Non-Independent and Identically Distributed (Non-IID) data, which results in inconsistent model performance. Moreover, relying on a central server also raises reliability and scalability issues. Decentralized Federated Learning (DFL) eliminates the central server, thereby fostering more robust and scalable collaboration. Despite the growing interest in DFL, a comprehensive review focusing on Non-IID challenges remains scarce. This article presents a Systematic Literature Review (SLR) of existing research on DFL under Non-IID settings. Studies were retrieved from six major academic publishers and categorized into four pillars: architecture, topology, optimization, and security. The SLR review provides insights into current trends and systematically summarizes real-world applications, commonly used datasets, and neural network models. This article also examines prevalent methods for conducting Non-IID experiments and evaluating performance metrics. By providing a structured analysis of the literature, experimental setups, and evaluation practices, this survey highlights key trends, uncovers research gaps, and proposes future directions for advancing DFL in Non-IID environments.
LLM-based conversational agents have become increasingly popular in recent years due to their novel capacity for natural, human-like dialogue interactions. However, mistrust in LLMs persists due to concerns about privacy, the potential for incorrect responses (often referred to as ’hallucinations’), and issues related to social bias. Previous AI research shows that anthropomorphic form positively influences users’ perceptions. However, this aspect remains under-explored in LLM-based conversational agent research. Our research features two anthropomorphic forms: embodied and behavioral. Embodied Anthropomorphic Form (EA) encompasses chatbot, chatbot with text-to-speech (TTS), and embodied conversational agent (ECA) interface designs. Behavioral Anthropomorphic Form (BA) involves LLMs instructed with and without Theory of Mind (ToM) principles. In an empirical evaluation, we explored how interplay between BA form and EA form, and vice-versa, affects users’ perceptions of LLM-based conversational agents on trust, anthropomorphism, presence, usability, and user experience. Our findings provide evidence of such effects, offering novel insight into the influence of both anthropomorphic forms on perceived anthropomorphism, presence, usability, user experience, and their positive impact on user trust in LLM-based conversational agents. However, the combined highest (i.e., ECA with ToM behaviors ) and lowest (i.e., Chatbot without ToM behaviors ) levels of both forms result in lower user trust, suggesting a complex relationship between embodiment and ToM behaviors that warrants further investigation.
The Surface Water and Ocean Topography mission (SWOT), equipped with the Ka-band Radar Interferometer (KaRIn), provides groundbreaking 2-D sea surface heights (SSHs), bringing new potential for optimizing the deflection of the vertical (DOVs). However, conventional DOV modeling-combining along- and cross-track geoid gradients with equal weights-fail to fully exploit the potential of SWOT/KaRIn observations and overlook the spatial variability in precision. We present a tailored method for optimizing DOVs estimation. The method combines geoid gradients in the along-track, cross-track, diagonal (forward and backward) directions with adaptive weighting. The refined weights are employed to exploit the potential of each geoid gradient based on the relationship between the standard deviation of SSHs and the significant wave height. To mitigate data gaps, prior and locally averaged geoid gradients are incorporated in the gaps and overlapping regions. SWOT/KaRIn-derived DOVs and gravity anomalies from the science-phase observations are validated against shipborne gravity in the Philippine Sea. Results indicate that the DOV model derived by the tailored method-particularly by combining triple-directional (along, cross and diagonally forward) geoid gradients with refined weights-achieves a 7.3 percent improvement in accuracy over the conventional method. The supplement of additional geoid gradients is critical for mitigating leakage errors caused by missing or reduced observations in the gap regions. Furthermore, the gravity anomaly model recovered from DOVs by stacking 17-cycle observations achieved an accuracy of 2.97 mGal, representing a 7.2 percent improvement over single-cycle observations. The clear advantages of SWOT/KaRIn observations are gradually emerging in marine gravity recovery.
This study employs a MHz burst-mode femtosecond laser to induce internal modification in 350-µm-thick n-type 4 H-SiC and to demonstrate wafer slicing using a single-/double-pulse (N = 1–2) configuration under a fixed average power of 4.5 W and a burst repetition rate of 600 kHz (burst energy ≈ 7.5 µJ). Microstructure, surface topography, and residual stress were evaluated using scanning electron microscopy, laser-scanning confocal microscopy, and Raman spectroscopy. Single-pass line modification shows that N = 1–2 provide higher sub-pulse peak intensity, promoting lateral-crack formation that facilitates separation, whereas N = 3–5 drive deeper modification without lateral cracks. Raman measurements reveal a characteristic tension–compression residual-stress distribution, with tensile stress above and predominantly compressive stress below the lateral cracks; for example, at N = 2, the stresses are + 77.5 MPa (tension) and − 51.7 MPa (compression) at y = ± 4 μm. Full-area slicing experiments demonstrate that N = 2 shortens the step-like period and reduces the sliced-surface roughness from Sz = 47.8 μm to 32.2 μm, while maintaining a tensile strength of 3.4 MPa. Comparative Raman analysis of the two sliced surfaces further shows that laser-induced amorphous Si/C and lattice disorder accumulate mainly above the crack plane, leading to an asymmetry between the upper and lower halves of the specimen. These results establish a minimal N = 2 (double-pulse) MHz-burst configuration that enhances lateral-crack interconnection and lowers surface roughness.
